Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations24000
Missing cells0
Missing cells (%)0.0%
Duplicate rows24
Duplicate rows (%)0.1%
Total size in memory4.4 MiB
Average record size in memory192.0 B

Variable types

Numeric21
Categorical3

Alerts

Dataset has 24 (0.1%) duplicate rowsDuplicates
BILL_AMT1 is highly overall correlated with BILL_AMT2 and 9 other fieldsHigh correlation
BILL_AMT2 is highly overall correlated with BILL_AMT1 and 11 other fieldsHigh correlation
BILL_AMT3 is highly overall correlated with BILL_AMT1 and 11 other fieldsHigh correlation
BILL_AMT4 is highly overall correlated with BILL_AMT1 and 14 other fieldsHigh correlation
BILL_AMT5 is highly overall correlated with BILL_AMT1 and 13 other fieldsHigh correlation
BILL_AMT6 is highly overall correlated with BILL_AMT1 and 11 other fieldsHigh correlation
PAY_1 is highly overall correlated with PAY_2 and 2 other fieldsHigh correlation
PAY_2 is highly overall correlated with BILL_AMT1 and 8 other fieldsHigh correlation
PAY_3 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_4 is highly overall correlated with BILL_AMT1 and 10 other fieldsHigh correlation
PAY_5 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_6 is highly overall correlated with BILL_AMT2 and 8 other fieldsHigh correlation
PAY_AMT1 is highly overall correlated with BILL_AMT1 and 5 other fieldsHigh correlation
PAY_AMT2 is highly overall correlated with BILL_AMT2 and 7 other fieldsHigh correlation
PAY_AMT3 is highly overall correlated with BILL_AMT4 and 7 other fieldsHigh correlation
PAY_AMT4 is highly overall correlated with BILL_AMT4 and 6 other fieldsHigh correlation
PAY_AMT5 is highly overall correlated with BILL_AMT4 and 6 other fieldsHigh correlation
PAY_AMT6 is highly overall correlated with BILL_AMT5 and 4 other fieldsHigh correlation
PAY_AMT2 is highly skewed (γ1 = 31.76959305) Skewed
PAY_1 has 11765 (49.0%) zeros Zeros
PAY_2 has 12579 (52.4%) zeros Zeros
PAY_3 has 12567 (52.4%) zeros Zeros
PAY_4 has 13087 (54.5%) zeros Zeros
PAY_5 has 13495 (56.2%) zeros Zeros
PAY_6 has 13005 (54.2%) zeros Zeros
BILL_AMT1 has 1607 (6.7%) zeros Zeros
BILL_AMT2 has 2016 (8.4%) zeros Zeros
BILL_AMT3 has 2296 (9.6%) zeros Zeros
BILL_AMT4 has 2554 (10.6%) zeros Zeros
BILL_AMT5 has 2806 (11.7%) zeros Zeros
BILL_AMT6 has 3222 (13.4%) zeros Zeros
PAY_AMT1 has 4186 (17.4%) zeros Zeros
PAY_AMT2 has 4308 (17.9%) zeros Zeros
PAY_AMT3 has 4801 (20.0%) zeros Zeros
PAY_AMT4 has 5148 (21.4%) zeros Zeros
PAY_AMT5 has 5396 (22.5%) zeros Zeros
PAY_AMT6 has 5743 (23.9%) zeros Zeros

Reproduction

Analysis started2025-08-20 19:56:45.532258
Analysis finished2025-08-20 19:57:59.255430
Duration1 minute and 13.72 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ)

Distinct80
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167876.4
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:57:59.405345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129907.45
Coefficient of variation (CV)0.77382796
Kurtosis0.55884867
Mean167876.4
Median Absolute Deviation (MAD)90000
Skewness0.99395368
Sum4.0290337 × 109
Variance1.6875947 × 1010
MonotonicityNot monotonic
2025-08-21T01:57:59.638171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 2698
 
11.2%
20000 1548
 
6.5%
30000 1286
 
5.4%
80000 1282
 
5.3%
200000 1204
 
5.0%
150000 889
 
3.7%
100000 834
 
3.5%
180000 791
 
3.3%
360000 713
 
3.0%
60000 649
 
2.7%
Other values (70) 12106
50.4%
ValueCountFrequency (%)
10000 395
 
1.6%
16000 1
 
< 0.1%
20000 1548
6.5%
30000 1286
5.4%
40000 187
 
0.8%
50000 2698
11.2%
60000 649
 
2.7%
70000 588
 
2.5%
80000 1282
5.3%
90000 512
 
2.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 4
< 0.1%
740000 2
 
< 0.1%
730000 2
 
< 0.1%
720000 2
 
< 0.1%
710000 5
< 0.1%
700000 8
< 0.1%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.6 KiB
2
14422 
1
9578 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 14422
60.1%
1 9578
39.9%

Length

2025-08-21T01:57:59.827881image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T01:57:59.990351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 14422
60.1%
1 9578
39.9%

Most occurring characters

ValueCountFrequency (%)
2 14422
60.1%
1 9578
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 14422
60.1%
1 9578
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 14422
60.1%
1 9578
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 14422
60.1%
1 9578
39.9%

EDUCATION
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8509583
Minimum0
Maximum6
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:58:00.127525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78736104
Coefficient of variation (CV)0.42538021
Kurtosis2.0180807
Mean1.8509583
Median Absolute Deviation (MAD)1
Skewness0.95419246
Sum44423
Variance0.61993741
MonotonicityNot monotonic
2025-08-21T01:58:00.272628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 11213
46.7%
1 8486
35.4%
3 3944
 
16.4%
5 217
 
0.9%
4 90
 
0.4%
6 39
 
0.2%
0 11
 
< 0.1%
ValueCountFrequency (%)
0 11
 
< 0.1%
1 8486
35.4%
2 11213
46.7%
3 3944
 
16.4%
4 90
 
0.4%
5 217
 
0.9%
6 39
 
0.2%
ValueCountFrequency (%)
6 39
 
0.2%
5 217
 
0.9%
4 90
 
0.4%
3 3944
 
16.4%
2 11213
46.7%
1 8486
35.4%
0 11
 
< 0.1%

MARRIAGE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.6 KiB
2
12808 
1
10897 
3
 
253
0
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 12808
53.4%
1 10897
45.4%
3 253
 
1.1%
0 42
 
0.2%

Length

2025-08-21T01:58:00.447150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T01:58:00.571744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 12808
53.4%
1 10897
45.4%
3 253
 
1.1%
0 42
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 12808
53.4%
1 10897
45.4%
3 253
 
1.1%
0 42
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 12808
53.4%
1 10897
45.4%
3 253
 
1.1%
0 42
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 12808
53.4%
1 10897
45.4%
3 253
 
1.1%
0 42
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 12808
53.4%
1 10897
45.4%
3 253
 
1.1%
0 42
 
0.2%

AGE
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.446708
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:58:00.755066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1808454
Coefficient of variation (CV)0.25900417
Kurtosis0.045823063
Mean35.446708
Median Absolute Deviation (MAD)6
Skewness0.73115373
Sum850721
Variance84.287922
MonotonicityNot monotonic
2025-08-21T01:58:00.938460image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1309
 
5.5%
27 1182
 
4.9%
28 1129
 
4.7%
30 1126
 
4.7%
31 975
 
4.1%
26 975
 
4.1%
25 953
 
4.0%
34 941
 
3.9%
33 933
 
3.9%
32 923
 
3.8%
Other values (46) 13554
56.5%
ValueCountFrequency (%)
21 52
 
0.2%
22 431
 
1.8%
23 770
3.2%
24 909
3.8%
25 953
4.0%
26 975
4.1%
27 1182
4.9%
28 1129
4.7%
29 1309
5.5%
30 1126
4.7%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 1
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 8
< 0.1%
69 12
0.1%
68 3
 
< 0.1%
67 11
< 0.1%

PAY_1
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.023875
Minimum-2
Maximum8
Zeros11765
Zeros (%)49.0%
Negative6843
Negative (%)28.5%
Memory size187.6 KiB
2025-08-21T01:58:01.121681image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1238065
Coefficient of variation (CV)-47.070427
Kurtosis2.9182819
Mean-0.023875
Median Absolute Deviation (MAD)1
Skewness0.76356482
Sum-573
Variance1.2629409
MonotonicityNot monotonic
2025-08-21T01:58:01.274367image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11765
49.0%
-1 4636
 
19.3%
1 2907
 
12.1%
-2 2207
 
9.2%
2 2127
 
8.9%
3 247
 
1.0%
4 55
 
0.2%
5 22
 
0.1%
8 17
 
0.1%
6 10
 
< 0.1%
ValueCountFrequency (%)
-2 2207
 
9.2%
-1 4636
 
19.3%
0 11765
49.0%
1 2907
 
12.1%
2 2127
 
8.9%
3 247
 
1.0%
4 55
 
0.2%
5 22
 
0.1%
6 10
 
< 0.1%
7 7
 
< 0.1%
ValueCountFrequency (%)
8 17
 
0.1%
7 7
 
< 0.1%
6 10
 
< 0.1%
5 22
 
0.1%
4 55
 
0.2%
3 247
 
1.0%
2 2127
 
8.9%
1 2907
 
12.1%
0 11765
49.0%
-1 4636
 
19.3%

PAY_2
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1405
Minimum-2
Maximum8
Zeros12579
Zeros (%)52.4%
Negative7924
Negative (%)33.0%
Memory size187.6 KiB
2025-08-21T01:58:01.421662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1942163
Coefficient of variation (CV)-8.4997599
Kurtosis1.6870491
Mean-0.1405
Median Absolute Deviation (MAD)0
Skewness0.81029667
Sum-3372
Variance1.4261525
MonotonicityNot monotonic
2025-08-21T01:58:01.571756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 12579
52.4%
-1 4901
 
20.4%
2 3089
 
12.9%
-2 3023
 
12.6%
3 257
 
1.1%
4 80
 
0.3%
1 24
 
0.1%
5 20
 
0.1%
7 18
 
0.1%
6 8
 
< 0.1%
ValueCountFrequency (%)
-2 3023
 
12.6%
-1 4901
 
20.4%
0 12579
52.4%
1 24
 
0.1%
2 3089
 
12.9%
3 257
 
1.1%
4 80
 
0.3%
5 20
 
0.1%
6 8
 
< 0.1%
7 18
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 18
 
0.1%
6 8
 
< 0.1%
5 20
 
0.1%
4 80
 
0.3%
3 257
 
1.1%
2 3089
 
12.9%
1 24
 
0.1%
0 12579
52.4%
-1 4901
 
20.4%

PAY_3
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.17
Minimum-2
Maximum8
Zeros12567
Zeros (%)52.4%
Negative8080
Negative (%)33.7%
Memory size187.6 KiB
2025-08-21T01:58:01.855036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1987118
Coefficient of variation (CV)-7.0512456
Kurtosis2.2187202
Mean-0.17
Median Absolute Deviation (MAD)0
Skewness0.86542473
Sum-4080
Variance1.4369099
MonotonicityNot monotonic
2025-08-21T01:58:02.011307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 12567
52.4%
-1 4805
 
20.0%
-2 3275
 
13.6%
2 3036
 
12.7%
3 190
 
0.8%
4 61
 
0.3%
7 23
 
0.1%
6 20
 
0.1%
5 16
 
0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
-2 3275
 
13.6%
-1 4805
 
20.0%
0 12567
52.4%
1 4
 
< 0.1%
2 3036
 
12.7%
3 190
 
0.8%
4 61
 
0.3%
5 16
 
0.1%
6 20
 
0.1%
7 23
 
0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 23
 
0.1%
6 20
 
0.1%
5 16
 
0.1%
4 61
 
0.3%
3 190
 
0.8%
2 3036
 
12.7%
1 4
 
< 0.1%
0 12567
52.4%
-1 4805
 
20.0%

PAY_4
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2215
Minimum-2
Maximum8
Zeros13087
Zeros (%)54.5%
Negative8086
Negative (%)33.7%
Memory size187.6 KiB
2025-08-21T01:58:02.163239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1717842
Coefficient of variation (CV)-5.2902223
Kurtosis3.5057726
Mean-0.2215
Median Absolute Deviation (MAD)0
Skewness1.0072972
Sum-5316
Variance1.3730783
MonotonicityNot monotonic
2025-08-21T01:58:02.304929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 13087
54.5%
-1 4608
 
19.2%
-2 3478
 
14.5%
2 2553
 
10.6%
3 134
 
0.6%
4 54
 
0.2%
7 46
 
0.2%
5 32
 
0.1%
6 4
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
-2 3478
 
14.5%
-1 4608
 
19.2%
0 13087
54.5%
1 2
 
< 0.1%
2 2553
 
10.6%
3 134
 
0.6%
4 54
 
0.2%
5 32
 
0.1%
6 4
 
< 0.1%
7 46
 
0.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 46
 
0.2%
6 4
 
< 0.1%
5 32
 
0.1%
4 54
 
0.2%
3 134
 
0.6%
2 2553
 
10.6%
1 2
 
< 0.1%
0 13087
54.5%
-1 4608
 
19.2%

PAY_5
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.26683333
Minimum-2
Maximum8
Zeros13495
Zeros (%)56.2%
Negative8116
Negative (%)33.8%
Memory size187.6 KiB
2025-08-21T01:58:02.454966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1332197
Coefficient of variation (CV)-4.2469194
Kurtosis3.9822157
Mean-0.26683333
Median Absolute Deviation (MAD)0
Skewness1.008783
Sum-6404
Variance1.2841868
MonotonicityNot monotonic
2025-08-21T01:58:02.604918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 13495
56.2%
-1 4493
 
18.7%
-2 3623
 
15.1%
2 2122
 
8.8%
3 139
 
0.6%
4 65
 
0.3%
7 47
 
0.2%
5 13
 
0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 3623
 
15.1%
-1 4493
 
18.7%
0 13495
56.2%
2 2122
 
8.8%
3 139
 
0.6%
4 65
 
0.3%
5 13
 
0.1%
6 2
 
< 0.1%
7 47
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 47
 
0.2%
6 2
 
< 0.1%
5 13
 
0.1%
4 65
 
0.3%
3 139
 
0.6%
2 2122
 
8.8%
0 13495
56.2%
-1 4493
 
18.7%
-2 3623
 
15.1%

PAY_6
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.29441667
Minimum-2
Maximum8
Zeros13005
Zeros (%)54.2%
Negative8543
Negative (%)35.6%
Memory size187.6 KiB
2025-08-21T01:58:02.771538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1463159
Coefficient of variation (CV)-3.8935158
Kurtosis3.3551147
Mean-0.29441667
Median Absolute Deviation (MAD)0
Skewness0.93198696
Sum-7066
Variance1.3140402
MonotonicityNot monotonic
2025-08-21T01:58:02.921560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 13005
54.2%
-1 4625
 
19.3%
-2 3918
 
16.3%
2 2207
 
9.2%
3 149
 
0.6%
4 37
 
0.2%
7 35
 
0.1%
6 15
 
0.1%
5 7
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 3918
 
16.3%
-1 4625
 
19.3%
0 13005
54.2%
2 2207
 
9.2%
3 149
 
0.6%
4 37
 
0.2%
5 7
 
< 0.1%
6 15
 
0.1%
7 35
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 35
 
0.1%
6 15
 
0.1%
5 7
 
< 0.1%
4 37
 
0.2%
3 149
 
0.6%
2 2207
 
9.2%
0 13005
54.2%
-1 4625
 
19.3%
-2 3918
 
16.3%

BILL_AMT1
Real number (ℝ)

High correlation  Zeros 

Distinct18660
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51105.607
Minimum-165580
Maximum964511
Zeros1607
Zeros (%)6.7%
Negative455
Negative (%)1.9%
Memory size187.6 KiB
2025-08-21T01:58:03.107679image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13479.75
median22217
Q366996.75
95-th percentile201013.1
Maximum964511
Range1130091
Interquartile range (IQR)63517

Descriptive statistics

Standard deviation73647.549
Coefficient of variation (CV)1.4410855
Kurtosis9.9041436
Mean51105.607
Median Absolute Deviation (MAD)21640.5
Skewness2.6698418
Sum1.2265346 × 109
Variance5.4239615 × 109
MonotonicityNot monotonic
2025-08-21T01:58:03.304733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1607
 
6.7%
390 199
 
0.8%
780 59
 
0.2%
326 56
 
0.2%
316 55
 
0.2%
2500 45
 
0.2%
396 40
 
0.2%
2400 30
 
0.1%
416 24
 
0.1%
1473 22
 
0.1%
Other values (18650) 21863
91.1%
ValueCountFrequency (%)
-165580 1
< 0.1%
-154973 1
< 0.1%
-15308 1
< 0.1%
-14386 1
< 0.1%
-11545 1
< 0.1%
-10682 1
< 0.1%
-9095 1
< 0.1%
-8187 1
< 0.1%
-7438 1
< 0.1%
-6676 1
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
746814 1
< 0.1%
630458 1
< 0.1%
621749 1
< 0.1%
613860 1
< 0.1%
608594 1
< 0.1%
604019 1
< 0.1%
589654 1
< 0.1%
588000 1
< 0.1%
581775 1
< 0.1%

BILL_AMT2
Real number (ℝ)

High correlation  Zeros 

Distinct18363
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49112.466
Minimum-69777
Maximum983931
Zeros2016
Zeros (%)8.4%
Negative532
Negative (%)2.2%
Memory size187.6 KiB
2025-08-21T01:58:03.504673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12907.75
median21107
Q363997.25
95-th percentile194914.8
Maximum983931
Range1053708
Interquartile range (IQR)61089.5

Descriptive statistics

Standard deviation71186.769
Coefficient of variation (CV)1.4494644
Kurtosis10.407104
Mean49112.466
Median Absolute Deviation (MAD)20717
Skewness2.7078559
Sum1.1786992 × 109
Variance5.067556 × 109
MonotonicityNot monotonic
2025-08-21T01:58:03.875498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2016
 
8.4%
390 189
 
0.8%
316 63
 
0.3%
780 59
 
0.2%
326 59
 
0.2%
396 40
 
0.2%
2500 38
 
0.2%
2400 33
 
0.1%
-200 23
 
0.1%
416 22
 
0.1%
Other values (18353) 21458
89.4%
ValueCountFrequency (%)
-69777 1
< 0.1%
-33350 1
< 0.1%
-30000 1
< 0.1%
-26214 1
< 0.1%
-24704 1
< 0.1%
-24702 1
< 0.1%
-22960 1
< 0.1%
-18618 1
< 0.1%
-18088 1
< 0.1%
-17810 1
< 0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
743970 1
< 0.1%
646770 1
< 0.1%
624475 1
< 0.1%
605943 1
< 0.1%
581775 1
< 0.1%
577681 1
< 0.1%
572834 1
< 0.1%
572677 1
< 0.1%
569577 1
< 0.1%

BILL_AMT3
Real number (ℝ)

High correlation  Zeros 

Distinct18134
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46878.503
Minimum-157264
Maximum1664089
Zeros2296
Zeros (%)9.6%
Negative530
Negative (%)2.2%
Memory size187.6 KiB
2025-08-21T01:58:04.142083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12594.25
median20009.5
Q360220.25
95-th percentile187527.35
Maximum1664089
Range1821353
Interquartile range (IQR)57626

Descriptive statistics

Standard deviation69315.274
Coefficient of variation (CV)1.4786153
Kurtosis21.915314
Mean46878.503
Median Absolute Deviation (MAD)19628.5
Skewness3.1517279
Sum1.1250841 × 109
Variance4.8046072 × 109
MonotonicityNot monotonic
2025-08-21T01:58:04.372416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2296
 
9.6%
390 230
 
1.0%
780 59
 
0.2%
316 55
 
0.2%
326 49
 
0.2%
396 36
 
0.1%
2400 30
 
0.1%
2500 29
 
0.1%
1000 22
 
0.1%
416 22
 
0.1%
Other values (18124) 21172
88.2%
ValueCountFrequency (%)
-157264 1
< 0.1%
-61506 1
< 0.1%
-46127 1
< 0.1%
-34041 1
< 0.1%
-24702 1
< 0.1%
-20320 1
< 0.1%
-17706 1
< 0.1%
-15910 1
< 0.1%
-15641 1
< 0.1%
-15000 1
< 0.1%
ValueCountFrequency (%)
1664089 1
< 0.1%
855086 1
< 0.1%
693131 1
< 0.1%
632041 1
< 0.1%
578971 1
< 0.1%
577957 1
< 0.1%
577015 1
< 0.1%
572677 1
< 0.1%
559712 1
< 0.1%
548020 1
< 0.1%

BILL_AMT4
Real number (ℝ)

High correlation  Zeros 

Distinct17733
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43172.528
Minimum-170000
Maximum891586
Zeros2554
Zeros (%)10.6%
Negative546
Negative (%)2.3%
Memory size187.6 KiB
2025-08-21T01:58:04.605146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12294
median19000
Q354837.25
95-th percentile173806.2
Maximum891586
Range1061586
Interquartile range (IQR)52543.25

Descriptive statistics

Standard deviation64075.909
Coefficient of variation (CV)1.4841825
Kurtosis10.979536
Mean43172.528
Median Absolute Deviation (MAD)18604
Skewness2.7832214
Sum1.0361407 × 109
Variance4.1057221 × 109
MonotonicityNot monotonic
2025-08-21T01:58:05.197104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2554
 
10.6%
390 213
 
0.9%
780 80
 
0.3%
316 59
 
0.2%
326 47
 
0.2%
396 35
 
0.1%
2400 31
 
0.1%
150 29
 
0.1%
1000 28
 
0.1%
416 27
 
0.1%
Other values (17723) 20897
87.1%
ValueCountFrequency (%)
-170000 1
< 0.1%
-81334 1
< 0.1%
-50616 1
< 0.1%
-34503 1
< 0.1%
-24303 1
< 0.1%
-22108 1
< 0.1%
-20320 1
< 0.1%
-17250 1
< 0.1%
-15910 1
< 0.1%
-15588 1
< 0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
628699 1
< 0.1%
616836 1
< 0.1%
565669 1
< 0.1%
563543 1
< 0.1%
548020 1
< 0.1%
530672 1
< 0.1%
518741 1
< 0.1%
516575 1
< 0.1%
504929 1
< 0.1%

BILL_AMT5
Real number (ℝ)

High correlation  Zeros 

Distinct17312
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40190.249
Minimum-53007
Maximum927171
Zeros2806
Zeros (%)11.7%
Negative537
Negative (%)2.2%
Memory size187.6 KiB
2025-08-21T01:58:05.926202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-53007
5-th percentile0
Q11718.5
median18059.5
Q350320
95-th percentile164449.45
Maximum927171
Range980178
Interquartile range (IQR)48601.5

Descriptive statistics

Standard deviation60385.391
Coefficient of variation (CV)1.5024886
Kurtosis11.823931
Mean40190.249
Median Absolute Deviation (MAD)17663.5
Skewness2.8327135
Sum9.6456599 × 108
Variance3.6463955 × 109
MonotonicityNot monotonic
2025-08-21T01:58:06.306064image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2806
 
11.7%
390 197
 
0.8%
780 75
 
0.3%
316 66
 
0.3%
326 51
 
0.2%
150 49
 
0.2%
396 39
 
0.2%
2400 32
 
0.1%
416 27
 
0.1%
-18 27
 
0.1%
Other values (17302) 20631
86.0%
ValueCountFrequency (%)
-53007 1
< 0.1%
-37594 1
< 0.1%
-36156 1
< 0.1%
-30481 1
< 0.1%
-20753 1
< 0.1%
-20320 1
< 0.1%
-20254 1
< 0.1%
-20006 1
< 0.1%
-19205 1
< 0.1%
-15306 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
587067 1
< 0.1%
547880 1
< 0.1%
530672 1
< 0.1%
524315 1
< 0.1%
516139 1
< 0.1%
514114 1
< 0.1%
508213 1
< 0.1%
505473 1
< 0.1%
501474 1
< 0.1%

BILL_AMT6
Real number (ℝ)

High correlation  Zeros 

Distinct16963
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38722.893
Minimum-339603
Maximum961664
Zeros3222
Zeros (%)13.4%
Negative570
Negative (%)2.4%
Memory size187.6 KiB
2025-08-21T01:58:06.703928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11238
median16846
Q349327.5
95-th percentile160083.05
Maximum961664
Range1301267
Interquartile range (IQR)48089.5

Descriptive statistics

Standard deviation59259.183
Coefficient of variation (CV)1.5303398
Kurtosis12.480919
Mean38722.893
Median Absolute Deviation (MAD)16546
Skewness2.8316038
Sum9.2934942 × 108
Variance3.5116508 × 109
MonotonicityNot monotonic
2025-08-21T01:58:06.938144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3222
 
13.4%
390 168
 
0.7%
780 69
 
0.3%
316 65
 
0.3%
150 58
 
0.2%
326 42
 
0.2%
396 39
 
0.2%
-18 28
 
0.1%
416 27
 
0.1%
540 25
 
0.1%
Other values (16953) 20257
84.4%
ValueCountFrequency (%)
-339603 1
< 0.1%
-209051 1
< 0.1%
-150953 1
< 0.1%
-94625 1
< 0.1%
-73895 1
< 0.1%
-57060 1
< 0.1%
-51183 1
< 0.1%
-45734 1
< 0.1%
-39046 1
< 0.1%
-36156 1
< 0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
514975 1
< 0.1%
513798 1
< 0.1%
511905 1
< 0.1%
499100 1
< 0.1%
498316 1
< 0.1%
496915 1
< 0.1%
496801 1
< 0.1%
489200 1
< 0.1%

PAY_AMT1
Real number (ℝ)

High correlation  Zeros 

Distinct6938
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5687.4898
Minimum0
Maximum873552
Zeros4186
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:58:07.321566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2102.5
Q35011
95-th percentile18361.35
Maximum873552
Range873552
Interquartile range (IQR)4011

Descriptive statistics

Standard deviation17011.022
Coefficient of variation (CV)2.9909542
Kurtosis450.30175
Mean5687.4898
Median Absolute Deviation (MAD)1937.5
Skewness15.453319
Sum1.3649976 × 108
Variance2.8937487 × 108
MonotonicityNot monotonic
2025-08-21T01:58:07.524676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4186
 
17.4%
2000 1083
 
4.5%
3000 707
 
2.9%
5000 552
 
2.3%
1500 395
 
1.6%
4000 339
 
1.4%
10000 335
 
1.4%
1000 291
 
1.2%
2500 246
 
1.0%
6000 236
 
1.0%
Other values (6928) 15630
65.1%
ValueCountFrequency (%)
0 4186
17.4%
1 7
 
< 0.1%
2 11
 
< 0.1%
3 12
 
0.1%
4 15
 
0.1%
5 11
 
< 0.1%
6 13
 
0.1%
7 9
 
< 0.1%
8 5
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
873552 1
< 0.1%
505000 1
< 0.1%
493358 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
302000 1
< 0.1%
300000 1
< 0.1%

PAY_AMT2
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct6904
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5928.4353
Minimum0
Maximum1684259
Zeros4308
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:58:07.726183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1820
median2013
Q35000
95-th percentile18862.95
Maximum1684259
Range1684259
Interquartile range (IQR)4180

Descriptive statistics

Standard deviation24232.317
Coefficient of variation (CV)4.0874727
Kurtosis1667.0386
Mean5928.4353
Median Absolute Deviation (MAD)1987
Skewness31.769593
Sum1.4228245 × 108
Variance5.872052 × 108
MonotonicityNot monotonic
2025-08-21T01:58:07.972176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4308
 
17.9%
2000 1009
 
4.2%
3000 687
 
2.9%
5000 584
 
2.4%
1000 485
 
2.0%
1500 415
 
1.7%
4000 324
 
1.4%
10000 247
 
1.0%
6000 235
 
1.0%
390 203
 
0.8%
Other values (6894) 15503
64.6%
ValueCountFrequency (%)
0 4308
17.9%
1 13
 
0.1%
2 15
 
0.1%
3 15
 
0.1%
4 10
 
< 0.1%
5 20
 
0.1%
6 7
 
< 0.1%
7 9
 
< 0.1%
8 7
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
1684259 1
< 0.1%
1227082 1
< 0.1%
1215471 1
< 0.1%
1024516 1
< 0.1%
580464 1
< 0.1%
401003 1
< 0.1%
388126 1
< 0.1%
385228 1
< 0.1%
368199 1
< 0.1%
361560 1
< 0.1%

PAY_AMT3
Real number (ℝ)

High correlation  Zeros 

Distinct6589
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5227.2455
Minimum0
Maximum896040
Zeros4801
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:58:08.205042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34512
95-th percentile17665
Maximum896040
Range896040
Interquartile range (IQR)4122

Descriptive statistics

Standard deviation17918.982
Coefficient of variation (CV)3.4279971
Kurtosis626.21117
Mean5227.2455
Median Absolute Deviation (MAD)1799
Skewness18.30276
Sum1.2545389 × 108
Variance3.2108993 × 108
MonotonicityNot monotonic
2025-08-21T01:58:08.430929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4801
 
20.0%
2000 992
 
4.1%
1000 864
 
3.6%
3000 699
 
2.9%
5000 565
 
2.4%
1500 403
 
1.7%
4000 303
 
1.3%
10000 246
 
1.0%
6000 190
 
0.8%
1200 184
 
0.8%
Other values (6579) 14753
61.5%
ValueCountFrequency (%)
0 4801
20.0%
1 11
 
< 0.1%
2 17
 
0.1%
3 10
 
< 0.1%
4 12
 
0.1%
5 14
 
0.1%
6 12
 
0.1%
7 14
 
0.1%
8 8
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
889043 1
< 0.1%
508229 1
< 0.1%
417588 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
332809 1
< 0.1%
326974 1
< 0.1%

PAY_AMT4
Real number (ℝ)

High correlation  Zeros 

Distinct6036
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4766.8034
Minimum0
Maximum621000
Zeros5148
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:58:08.638733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1285
median1500
Q34040.25
95-th percentile16000
Maximum621000
Range621000
Interquartile range (IQR)3755.25

Descriptive statistics

Standard deviation15421.988
Coefficient of variation (CV)3.2352893
Kurtosis312.61672
Mean4766.8034
Median Absolute Deviation (MAD)1500
Skewness13.556888
Sum1.1440328 × 108
Variance2.3783772 × 108
MonotonicityNot monotonic
2025-08-21T01:58:08.870600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5148
 
21.4%
1000 1119
 
4.7%
2000 938
 
3.9%
3000 705
 
2.9%
5000 640
 
2.7%
1500 339
 
1.4%
4000 313
 
1.3%
10000 280
 
1.2%
2500 206
 
0.9%
500 203
 
0.8%
Other values (6026) 14109
58.8%
ValueCountFrequency (%)
0 5148
21.4%
1 18
 
0.1%
2 15
 
0.1%
3 9
 
< 0.1%
4 19
 
0.1%
5 9
 
< 0.1%
6 13
 
0.1%
7 9
 
< 0.1%
8 6
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
621000 1
< 0.1%
528897 1
< 0.1%
497000 1
< 0.1%
400046 1
< 0.1%
331788 1
< 0.1%
320008 1
< 0.1%
313094 1
< 0.1%
292962 1
< 0.1%
292462 1
< 0.1%
291227 1
< 0.1%

PAY_AMT5
Real number (ℝ)

High correlation  Zeros 

Distinct5985
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4774.8618
Minimum0
Maximum426529
Zeros5396
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:58:09.070564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1239.75
median1500
Q34024.25
95-th percentile15576.3
Maximum426529
Range426529
Interquartile range (IQR)3784.5

Descriptive statistics

Standard deviation15400.224
Coefficient of variation (CV)3.2252712
Kurtosis186.77055
Mean4774.8618
Median Absolute Deviation (MAD)1500
Skewness11.379962
Sum1.1459668 × 108
Variance2.371669 × 108
MonotonicityNot monotonic
2025-08-21T01:58:09.293033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5396
 
22.5%
2000 1061
 
4.4%
1000 1054
 
4.4%
3000 762
 
3.2%
5000 652
 
2.7%
1500 329
 
1.4%
4000 328
 
1.4%
10000 269
 
1.1%
6000 207
 
0.9%
500 197
 
0.8%
Other values (5975) 13745
57.3%
ValueCountFrequency (%)
0 5396
22.5%
1 16
 
0.1%
2 9
 
< 0.1%
3 9
 
< 0.1%
4 9
 
< 0.1%
5 8
 
< 0.1%
6 7
 
< 0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
379267 1
< 0.1%
332000 1
< 0.1%
330982 1
< 0.1%
326889 1
< 0.1%
317077 1
< 0.1%
310135 1
< 0.1%
300000 1
< 0.1%

PAY_AMT6
Real number (ℝ)

High correlation  Zeros 

Distinct6047
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5280.9239
Minimum0
Maximum528666
Zeros5743
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size187.6 KiB
2025-08-21T01:58:09.487100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1111
median1500
Q34010.5
95-th percentile17387.5
Maximum528666
Range528666
Interquartile range (IQR)3899.5

Descriptive statistics

Standard deviation18214.205
Coefficient of variation (CV)3.4490565
Kurtosis174.85209
Mean5280.9239
Median Absolute Deviation (MAD)1500
Skewness10.877723
Sum1.2674217 × 108
Variance3.3175727 × 108
MonotonicityNot monotonic
2025-08-21T01:58:09.689415image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5743
23.9%
1000 1043
 
4.3%
2000 1012
 
4.2%
3000 737
 
3.1%
5000 657
 
2.7%
1500 347
 
1.4%
4000 335
 
1.4%
10000 285
 
1.2%
500 208
 
0.9%
6000 180
 
0.8%
Other values (6037) 13453
56.1%
ValueCountFrequency (%)
0 5743
23.9%
1 19
 
0.1%
2 8
 
< 0.1%
3 10
 
< 0.1%
4 11
 
< 0.1%
5 5
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
527143 1
< 0.1%
443001 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
345293 1
< 0.1%
308000 1
< 0.1%

default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size187.6 KiB
0
18694 
1
5306 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18694
77.9%
1 5306
 
22.1%

Length

2025-08-21T01:58:09.856523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T01:58:10.005133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 18694
77.9%
1 5306
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 18694
77.9%
1 5306
 
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18694
77.9%
1 5306
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18694
77.9%
1 5306
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18694
77.9%
1 5306
 
22.1%

Interactions

2025-08-21T01:57:54.895333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:51.724249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:56.072072image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:59.080109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:02.254965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:05.411558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:08.353591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:11.439948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:14.366487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:17.432389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:20.405200image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:23.685653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:26.683148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:30.044657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:33.045965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:36.114421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:39.180405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:42.107404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:45.424035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:48.444093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:51.782773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:55.033823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:51.920229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:56.223499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:59.347226image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:02.402207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:05.550387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:08.500186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:11.582861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:14.491127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:17.582441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:20.546331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:23.811123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:26.831136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:30.197301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:33.186038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:36.252551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:39.316114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:42.381220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:45.581253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:48.617599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:51.962245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:55.180453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:52.833358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:56.368858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:59.502674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:02.543033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:05.681131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:08.637439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:11.716783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:14.648878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:17.717983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:20.697779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:23.964191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:26.999514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:30.323994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:33.324286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:36.404280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:39.458326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:42.609565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:45.747283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:48.766520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:52.090770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:55.334101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:53.010719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:56.523777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:59.653291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:02.682125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:05.837580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:08.783887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:11.843736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:14.783262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:17.870861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:20.829251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:24.117982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:27.158218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:30.482345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:33.466643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:36.546587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:39.591377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:42.759250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:45.896116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:48.931170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:52.244742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:55.480850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:53.197761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:56.655135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:59.779501image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:02.816995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:05.958203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:08.910827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:11.999802image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:14.931958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:17.999870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:20.982535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:24.255877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:27.307254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:30.624692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:33.617428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:36.703022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:39.724705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:42.906434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:46.064904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:49.212663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:52.378500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:55.903272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:53.373431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:56.781348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:59.936355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:02.952239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:06.106188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:09.053365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:12.134111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:15.068432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:18.140985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:21.109070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:24.395482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:27.458551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:30.770015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:33.764142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:36.841796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:39.876555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:43.044508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:46.194520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:49.342651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:52.517798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:56.042495image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:53.591496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:56.934986image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:00.093441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:03.082676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:06.234395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:09.179664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:12.268925image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:15.203298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:18.259236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:21.266130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:24.532396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:27.598975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:30.894086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:33.896772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:36.978756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-08-21T01:57:13.643931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:16.729501image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:19.695094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:22.743933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:25.965021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:29.156555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:32.329910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:35.277897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:38.437001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:41.408903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:44.695395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:47.762553image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:50.993618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:54.185301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:57.645489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:55.412658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:58.517682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:01.699969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:04.736908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:07.800005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:10.895322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:13.796636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:16.887522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:19.846190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:23.008594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:26.115100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:29.461092image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:32.493161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:35.429923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:38.595942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:41.561745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:44.844210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:47.911192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:51.144722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:54.322625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:57.782814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:55.600862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:58.658427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:01.835252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:04.865085image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:07.936434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:11.011942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:13.929333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:17.018179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:19.983222image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:23.167417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:26.253924image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:29.600033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:32.622525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:35.564665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:38.742882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:41.691218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:44.995862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:48.023639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:51.289876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:54.473522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:57.926766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:55.765812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:58.805369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:01.985645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:05.132746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:08.077701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:11.169620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:14.082184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:17.159256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:20.116763image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:23.396996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:26.401775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:29.764585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:32.763507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:35.707272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:38.895640image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:41.829570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:45.144654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:48.179226image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:51.462246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:54.622155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:58.099096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:55.919653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:56:58.929603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:02.118077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:05.268463image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:08.198734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:11.301830image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:14.218019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:17.276780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:20.264215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:23.531671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:26.536658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:29.905535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:32.908904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:35.841620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:39.035131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:41.969410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:45.291600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:48.316190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:51.627754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-21T01:57:54.749790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-08-21T01:58:10.153836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AGEBILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6EDUCATIONLIMIT_BALMARRIAGEPAY_1PAY_2PAY_3PAY_4PAY_5PAY_6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6SEXdefault
AGE1.0000.0020.0040.003-0.003-0.001-0.0010.1600.1860.286-0.066-0.083-0.083-0.078-0.084-0.0790.0390.0480.0290.0390.0360.0430.1010.043
BILL_AMT10.0021.0000.9110.8570.8080.7690.7360.0950.0510.0140.3220.5760.5270.5130.5000.4890.5050.4730.4410.4420.4280.4130.0240.025
BILL_AMT20.0040.9111.0000.9080.8500.8040.7660.0920.0470.0060.3360.5540.5910.5590.5380.5240.6370.5010.4690.4610.4520.4310.0290.027
BILL_AMT30.0030.8570.9081.0000.9040.8490.8040.0810.0590.0040.3210.5210.5590.6210.5890.5610.5520.6400.4920.4880.4800.4580.0150.000
BILL_AMT4-0.0030.8080.8500.9041.0000.9040.8490.0690.0730.0110.3150.5010.5350.5950.6510.6060.5160.5570.6340.5080.5090.4830.0210.019
BILL_AMT5-0.0010.7690.8040.8490.9041.0000.9020.0600.0820.0170.3040.4800.5090.5620.6190.6690.4860.5190.5500.6480.5270.5110.0220.018
BILL_AMT6-0.0010.7360.7660.8040.8490.9021.0000.0550.0890.0120.2940.4620.4860.5340.5810.6310.4590.4910.5200.5690.6680.5280.0190.015
EDUCATION0.1600.0950.0920.0810.0690.0600.0551.000-0.2600.1140.1320.1680.1590.1500.1350.122-0.041-0.046-0.043-0.044-0.053-0.0520.0270.072
LIMIT_BAL0.1860.0510.0470.0590.0730.0820.089-0.2601.0000.059-0.294-0.339-0.328-0.305-0.281-0.2600.2740.2800.2840.2840.2960.3200.0720.153
MARRIAGE0.2860.0140.0060.0040.0110.0170.0120.1140.0591.0000.0320.0320.0300.0330.0310.0290.0340.0220.0220.0330.0110.0000.0250.035
PAY_1-0.0660.3220.3360.3210.3150.3040.2940.132-0.2940.0321.0000.6280.5470.5180.4900.465-0.095-0.055-0.046-0.033-0.020-0.0410.0580.422
PAY_2-0.0830.5760.5540.5210.5010.4800.4620.168-0.3390.0320.6281.0000.7990.7120.6730.6360.0240.0880.0920.0970.1030.0860.0690.334
PAY_3-0.0830.5270.5910.5590.5350.5090.4860.159-0.3280.0300.5470.7991.0000.8000.7180.6720.2180.0400.1070.1190.1260.0990.0660.290
PAY_4-0.0780.5130.5590.6210.5950.5620.5340.150-0.3050.0330.5180.7120.8001.0000.8220.7320.1880.2480.0700.1430.1640.1420.0640.278
PAY_5-0.0840.5000.5380.5890.6510.6190.5810.135-0.2810.0310.4900.6730.7180.8221.0000.8200.1780.2240.2620.1070.1900.1730.0520.271
PAY_6-0.0790.4890.5240.5610.6060.6690.6310.122-0.2600.0290.4650.6360.6720.7320.8201.0000.1780.2040.2370.2870.1450.2000.0490.251
PAY_AMT10.0390.5050.6370.5520.5160.4860.459-0.0410.2740.034-0.0950.0240.2180.1880.1780.1781.0000.5180.5190.4910.4720.4560.0000.027
PAY_AMT20.0480.4730.5010.6400.5570.5190.491-0.0460.2800.022-0.0550.0880.0400.2480.2240.2040.5181.0000.5160.5250.5020.4910.0000.008
PAY_AMT30.0290.4410.4690.4920.6340.5500.520-0.0430.2840.022-0.0460.0920.1070.0700.2620.2370.5190.5161.0000.5140.5380.5020.0160.023
PAY_AMT40.0390.4420.4610.4880.5080.6480.569-0.0440.2840.033-0.0330.0970.1190.1430.1070.2870.4910.5250.5141.0000.5340.5470.0000.021
PAY_AMT50.0360.4280.4520.4800.5090.5270.668-0.0530.2960.011-0.0200.1030.1260.1640.1900.1450.4720.5020.5380.5341.0000.5460.0130.036
PAY_AMT60.0430.4130.4310.4580.4830.5110.528-0.0520.3200.000-0.0410.0860.0990.1420.1730.2000.4560.4910.5020.5470.5461.0000.0170.028
SEX0.1010.0240.0290.0150.0210.0220.0190.0270.0720.0250.0580.0690.0660.0640.0520.0490.0000.0000.0160.0000.0130.0171.0000.042
default0.0430.0250.0270.0000.0190.0180.0150.0720.1530.0350.4220.3340.2900.2780.2710.2510.0270.0080.0230.0210.0360.0280.0421.000

Missing values

2025-08-21T01:57:58.373752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-21T01:57:58.907126image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_1PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default
0200002212422-1-1-2-23913.03102.0689.00.00.00.00.0689.00.00.00.00.01
1900002223400000029239.014027.013559.014331.014948.015549.01518.01500.01000.01000.01000.05000.00
2500002213700000046990.048233.049291.028314.028959.029547.02000.02019.01200.01100.01069.01000.00
35000012157-10-10008617.05670.035835.020940.019146.019131.02000.036681.010000.09000.0689.0679.00
4500001123700000064400.057069.057608.019394.019619.020024.02500.01815.0657.01000.01000.0800.00
5100000222230-1-100-111876.0380.0601.0221.0-159.0567.0380.0601.00.0581.01687.01542.00
61400002312800200011285.014096.012108.012211.011793.03719.03329.00.0432.01000.01000.01000.00
72000013235-2-2-2-2-1-10.00.00.00.013007.013912.00.00.00.013007.01122.00.00
82000002323400200-111073.09787.05535.02513.01828.03731.02306.012.050.0300.03738.066.00
926000021251-1-1-1-1-1212261.021670.09966.08517.022287.013668.021818.09966.08583.022301.00.03640.00
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_1PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default
2399025000011134000000279640.0252913.0243075.0245750.0175005.0179687.065000.08800.09011.06000.07000.06009.00
2399115000011235-1-1-1-1-1-23425.09009.0-3.0780.00.00.09054.00.0783.00.00.00.00
2399214000012141000000138325.0137142.0139110.0138262.049675.046121.06000.07000.04228.01505.02000.02000.00
239931000013143000-2-2-28802.010400.00.00.00.00.02000.00.00.00.00.00.00
23994100000112380-1-10003042.01427.0102996.070626.069473.055004.02000.0111784.04000.03000.02000.02000.00
23995800001223422222272557.077708.079384.077519.082607.081158.07000.03500.00.07000.00.04000.01
2399615000013243-1-1-1-1001683.01828.03502.08979.05190.00.01837.03526.08998.0129.00.00.00
239973000012237432-1003565.03356.02758.020878.020582.019357.00.00.022000.04200.02000.03100.01
2399880000131411-1000-1-1645.078379.076304.052774.011855.048944.085900.03409.01178.01926.052964.01804.01
23999500001214600000047929.048905.049764.036535.032428.015313.02078.01800.01430.01000.01000.01000.01

Duplicate rows

Most frequently occurring

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_1PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default# duplicates
020000122242244441650.01650.01650.01650.01650.01650.00.00.00.00.00.00.012
150000122261-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.002
250000212231-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.002
38000022131-2-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.002
48000022225-2-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.002
58000023142-2-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.002
690000212311-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.002
7150000211311-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.002
8150000211381-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.012
9150000212281-2-2-2-2-20.00.00.00.00.00.00.00.00.00.00.00.002